Research Papers

Statistic Determination of Storage Capacity for Photovoltaic Energy Imbalance Mitigation

[+] Author and Article Information
Corrado Giammanco

Department of Electronic Engineering,
University of Rome Tor Vergata,
Via del Politecnico, 1,
Rome 00133, Italy
e-mail: corado.giammanco@uniroma2.it

Marco Pierro, Cristina Cornaro

Department of Enterprise Engineering,
University of Rome Tor Vergata,
Via del Politecnico, 1,
Rome 00133, Italy

Aldo di Carlo

Department of Electronic Engineering,
University of Rome Tor Vergata,
Via del Politecnico, 1,
Rome 00133, Italy

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received March 20, 2015; final manuscript received September 2, 2015; published online October 29, 2015. Assoc. Editor: M. Keith Sharp.

J. Sol. Energy Eng 138(1), 011002 (Oct 29, 2015) (5 pages) Paper No: SOL-15-1073; doi: 10.1115/1.4031801 History: Received March 20, 2015; Revised September 02, 2015

This paper describes a methodology to evaluate the storage capacity that should support a photo-voltaic (PV) power plant in order to reduce the power imbalance generated by the forecast error. This is obtained through a probabilistic analysis performed on an ensemble of synthetic data. The synthetic data signals are generated starting from at least 1 year of measured data and reproduce the same statistical distribution and the same Fourier power spectrum (FPS) shape.

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Fig. 1

The figures show the forecast error probability density for the four seasonal behavior: autumn (a), winter (b), spring (c), and summer (d). The continuous lines show the best fits obtained with the Gauss–Lorentz function.

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Fig. 2

Module of the fast-Fourier transform of the autumnal error signal. The abscissa follows the usual convection of FFT routines.

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Fig. 3

In the top panel (a), the black curve represents the probability density for the real signal and the overlapped red curve (for color version, grey curve for black and white version) represents the values obtained for a synthetic one. In the bottom panel (b), the related Fourier power spectra are superimposed. The real signal is again in black and the synthetic one is in red.

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Fig. 4

Probability of MAE reduction for a fraction of 50%, 40%, or 30%, as a function of the installed capacity assuming a storage efficiency of 0.8

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Fig. 5

Probability to reduce the MAE to the fraction reported in abscissa, given an installed capacity of 0.1–3 Wh/Wp

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Fig. 6

Fraction of MAE reduction with probability 1, as a function of the installed capacity. The three lines are obtained assuming: first, that every year the forecast ability will be the same obtained by the measured data; second, that the weather stability determines an improvement of 10%; and finally, that there is a worsening of 10%.




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